The three most useful AI features for almost any app are semantic search that understands what users mean even when their words or dialect differ, summaries that turn long text (reviews, support tickets, chats) into a few clear points, and recommendations that suggest the right next product or service. Start with one feature tied to a clear problem, ground it in your own data, and set cost and privacy limits before launch.
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Which AI features are worth adding to your app?
If you are wondering how to add AI features to your app, the short answer is: focus on three proven features that serve users directly, smart search, summaries and recommendations. None of them requires training your own model. They use a ready-made large language model connected to your own data: your products, services, customer reviews and conversations.
The common mistake is bolting a generic 'AI assistant' onto a corner of the screen that knows nothing about the business. Users try it once and ignore it. Search that finds a 'plain black abaya' when a customer types a misspelled, dialect version of it, on the other hand, instantly makes the app feel like it understands them.
| Feature | Problem it solves | Gulf examples |
|---|---|---|
| Semantic search | Customers can't find things because their words differ from yours | Perfume store, spare-parts catalog, home-services directory |
| Summaries | Long text nobody reads | Review summary on a product, support ticket summary, WhatsApp chat summary for staff |
| Recommendations | Customers don't know what to pick next | 'Often bought together', a suggested salon package, a side dish at a restaurant |
| Auto-classification | Manual sorting eats team time | Tagging orders and complaints by type and priority |
Smart search: making your app understand meaning, not just letters
Traditional search matches characters. In Arabic that is a real problem: hamza and no hamza, taa marbuta written as haa, three different words for 'phone', plus younger users typing Arabic in Latin letters (Arabizi) such as '3abaya'. The result is a 'no results' page even though the product is right there.
Semantic search turns every product and every search phrase into a numeric representation called an embedding, then looks for the closest meaning. Those embeddings live in a vector database or an extension inside your existing database. That is how a search for 'Eid gift for my mom' finds the right items even if no product description contains those words.
The best results come from combining methods
- Keyword search for exact things: model numbers, brand names, product codes.
- Semantic search for descriptive phrases, dialects and typos.
- Clear filters for price, size, city and availability, because AI does not replace them.
- Arabic normalization before searching: hamza forms, alif maqsura, taa marbuta and elongation.
If your product descriptions are thin (just a name), semantic search will not work miracles. Invest in good Arabic descriptions, or ask AI to draft them and review them yourself.
Summaries: saving time for customers and your team
Summarization is one of the safest and most useful features, because the model works on text that is right in front of it and needs no outside knowledge. That keeps hallucinations low, as long as you explicitly tell it not to add anything that is not in the source.
- 1Decide what gets summarized, and for whom
A review summary for a shopper is different from a ticket summary for an agent. Write the goal in one sentence.
- 2Fix the format
For example: three positives, two negatives and a one-line verdict. A fixed format is easy to display in the UI.
- 3Match the language to the user
If reviews come in mixed dialects, ask for a summary in clear Arabic, plus English if the app is bilingual.
- 4Store summaries instead of regenerating them
A product's review summary does not change every minute. Generate it when new reviews arrive and save it; costs drop and pages load faster.
- 5Show the source
Put a 'read all reviews' link under the summary. Transparency builds trust.
A strong internal use: when a support agent picks up a long WhatsApp conversation, a three-line summary appears at the top: what the customer wants, what happened before, and what is needed now.
Recommendations: when you need AI and when rules are enough
Not every recommendation needs a model. In the first weeks, when you have little purchase data, simple rules are often more accurate and cheaper. AI pulls ahead once data builds up, or when you want to understand a free-text request from the customer.
- 'Often bought together' from past orders
- Items in the same category and price range
- Most ordered in your city this week
- Seasonal picks: Ramadan, Eid, National Day
- Easy to understand and adjust from the admin panel
- Understand free requests like 'a light perfume for the office'
- Connect items that are similar in meaning, not just category
- Explain the suggestion in one short line
- Need good product descriptions and enough data
- Need cost and quality monitoring
What happens behind the scenes: data, privacy and cost
Each of these features follows roughly the same path: the app gathers the right context from the database, sends it to the model with clear instructions, then checks the result before showing it. This approach is called RAG, and it is what grounds answers in your data instead of general knowledge.
| Feature | Data it needs | Risk level | Key safeguard |
|---|---|---|---|
| Semantic search | Product or service descriptions | Low | Arabic normalization and clear filters |
| Review summaries | Review text | Low to medium | Forbid facts not present in the reviews |
| Support chat summaries | Conversations that may contain personal data | Medium | Mask phone numbers and addresses before sending |
| Personalized recommendations | Browsing and purchase history | Medium | Clear consent and an updated privacy policy |
Cost is measured in tokens sent and received, so caching and generating summaries once make a big difference. We break this down in What AI features cost to run.
How to build these features by chatting with Fanann
With Fanann you do not pick an embeddings library or write integration code. You describe the feature in your own words, the Fanann Agent plans the tables, endpoints and UI, builds the feature inside your app with a managed database, and tests it. Start with one feature, try it, then add the next.
Add two features to my store. First: smart search that understands Arabic, Gulf dialects, typos and Arabic typed in Latin letters, combining keyword search (for brand names and product codes) with meaning-based search, plus filters for price, category and availability. Normalize Arabic text before searching (hamza forms, taa marbuta, alif maqsura). Second: a review summary shown above each product's reviews: three positives, two negatives and a one-line verdict, in Arabic and English, generated only when a new review arrives and then stored. The summary must not add anything that is not in the reviews. Add a daily AI usage cap in the admin panel. Show me the plan before building.
Try it in FanannA pre-launch checklist for any AI feature
- Test with real Arabic: dialects, typos, English words mid-sentence.
- Test empty states: a product with no reviews, a search with no results, a new user with no history.
- Set [guardrails](/{lang}/wiki/glossary/guardrails/): what is the model never allowed to say or do?
- Cap the cost: a daily limit and an alert as you approach it.
- Measure impact: did successful searches go up? Did ticket handling time go down? Compare before and after.
- Have a fallback: if the model provider is down, search falls back to plain keyword search instead of an error page.
The best AI feature is one users don't notice as AI. They just notice the app got easier.
Next step: pick one feature from the table above, build it this week, and watch how it is used for two weeks before adding another. If you are considering a full chat assistant, read our guide to an AI chatbot for your website.
Key takeaways
- Start with one feature that solves a problem users feel, not 'AI' for the headline.
- Semantic search with embeddings fixes classic Arabic search problems: hamza variants, taa marbuta, dialects and Arabizi.
- Summaries work when you fix the output format and forbid the model from adding facts that are not in the text.
- Simple rule-based recommendations are often enough at first; add AI once you have enough data.
- Set cost caps, protect personal data and test in real Arabic before any launch.
Frequently asked questions
How do I add AI to my app without coding?
Do I need to train my own AI model?
Can AI search understand Arabic dialects?
How much does it cost to add AI features to an app?
Is it safe to send customer data to an AI model?
Ask your AI assistant about this
Copy this prompt into the AI assistant you use, then add the details of your own case.
I am reading this guide: “How to add AI features to your app: search, summaries and recommendations” https://fanann.com/en/blog/ai-features-in-your-app/ My question: how to add ai features to my app Use that page as a source and explain the answer step by step for my situation, tell me what to check before I start, and cite the page link.